Journal articles on the topic 'User Interest Profiling'
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Liang, Shangsong. "Collaborative, Dynamic and Diversified User Profiling." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 4269–76. http://dx.doi.org/10.1609/aaai.v33i01.33014269.
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In this paper, we study the problem of dynamic user profiling in the context of streams of short texts. Previous work on user profiling works with long documents, do not consider collaborative information, and do not diversify the keywords for profiling users’ interests. In contrast, we address the problem by proposing a user profiling algorithm (UPA), which consists of two models: the proposed collaborative interest tracking topic model (CITM) and the proposed streaming keyword diversification model (SKDM). UPA first utilizes CITM to collaboratively track each user’s and his followees’ dynamic interest distributions in the context of streams of short texts, and then utilizes SKDM to obtain top-k relevant and diversified keywords to profile users’ interests at a specific point in time. Experiments were conducted on a Twitter dataset and we found that UPA outperforms state-of-the-art non-dynamic and dynamic user profiling algorithms.
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Lu, Junru, Le Chen, Kongming Meng, Fengyi Wang, Jun Xiang, Nuo Chen, Xu Han, and Binyang Li. "Identifying User Profile by Incorporating Self-Attention Mechanism based on CSDN Data Set." Data Intelligence 1, no. 2 (May 2019): 160–75. http://dx.doi.org/10.1162/dint_a_00009.
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With the popularity of social media, there has been an increasing interest in user profiling and its applications nowadays. This paper presents our system named UIR-SIST for User Profiling Technology Evaluation Campaign in SMP CUP 2017. UIR-SIST aims to complete three tasks, including keywords extraction from blogs, user interests labeling and user growth value prediction. To this end, we first extract keywords from a user's blog, including the blog itself, blogs on the same topic and other blogs published by the same user. Then a unified neural network model is constructed based on a convolutional neural network (CNN) for user interests tagging. Finally, we adopt a stacking model for predicting user growth value. We eventually receive the sixth place with evaluation scores of 0.563, 0.378 and 0.751 on the three tasks, respectively.
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Tang, Xiaoyu, and Qingtian Zeng. "Keyword clustering for user interest profiling refinement within paper recommender systems." Journal of Systems and Software 85, no. 1 (January 2012): 87–101. http://dx.doi.org/10.1016/j.jss.2011.07.029.
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You, Quanzeng, Sumit Bhatia, and Jiebo Luo. "A picture tells a thousand words—About you! User interest profiling from user generated visual content." Signal Processing 124 (July 2016): 45–53. http://dx.doi.org/10.1016/j.sigpro.2015.10.032.
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Yang, Chunfeng, Yipeng Zhou, and Dah Ming Chiu. "Who Are Like-Minded: Mining User Interest Similarity in Online Social Networks." Proceedings of the International AAAI Conference on Web and Social Media 10, no. 1 (August 4, 2021): 731–34. http://dx.doi.org/10.1609/icwsm.v10i1.14779.
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In this paper, we mine and learn to predict how similar a pair of users’ interests towards videos are, based on demographic, social and interest information of these users. We use the video access patterns of active users as ground truth. We adopt tag-based user profiling to establish this ground truth. We then show the effectiveness of the different features, and their combinations and derivatives, in predicting user interest similarity, based on different machine-learning methods for combining multiple features. We propose a hybrid tree-encoded linear model for combining the features, and show that it out-performs other linear and tree-based models. Our methods can be used to predict user interest similarity when the ground-truth is not available, e.g. for new users, or inactive users whose interests may have changed from old access data, and is useful for video recommendation.
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Shen, Jiaxing, Jiannong Cao, Oren Lederman, Shaojie Tang, and Alex “Sandy” Pentland. "User Profiling Based on Nonlinguistic Audio Data." ACM Transactions on Information Systems 40, no. 1 (January 31, 2022): 1–23. http://dx.doi.org/10.1145/3474826.
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User profiling refers to inferring people’s attributes of interest ( AoIs ) like gender and occupation, which enables various applications ranging from personalized services to collective analyses. Massive nonlinguistic audio data brings a novel opportunity for user profiling due to the prevalence of studying spontaneous face-to-face communication. Nonlinguistic audio is coarse-grained audio data without linguistic content. It is collected due to privacy concerns in private situations like doctor-patient dialogues. The opportunity facilitates optimized organizational management and personalized healthcare, especially for chronic diseases. In this article, we are the first to build a user profiling system to infer gender and personality based on nonlinguistic audio. Instead of linguistic or acoustic features that are unable to extract, we focus on conversational features that could reflect AoIs. We firstly develop an adaptive voice activity detection algorithm that could address individual differences in voice and false-positive voice activities caused by people nearby. Secondly, we propose a gender-assisted multi-task learning method to combat dynamics in human behavior by integrating gender differences and the correlation of personality traits. According to the experimental evaluation of 100 people in 273 meetings, we achieved 0.759 and 0.652 in F1-score for gender identification and personality recognition, respectively.
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Movahedian, Hamed, and Mohammad Reza Khayyambashi. "Folksonomy-based user interest and disinterest profiling for improved recommendations: An ontological approach." Journal of Information Science 40, no. 5 (June 19, 2014): 594–610. http://dx.doi.org/10.1177/0165551514539870.
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Godoy, D., and A. Amandi. "Interest Drifts in User Profiling: A Relevance-Based Approach and Analysis of Scenarios." Computer Journal 52, no. 7 (January 4, 2008): 771–88. http://dx.doi.org/10.1093/comjnl/bxm107.
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Worzella, Tracy, Matt Butzler, Jacquelyn Hennek, Seth Hanson, Laura Simdon, Said Goueli, Cris Cowan, and Hicham Zegzouti. "A Flexible Workflow for Automated Bioluminescent Kinase Selectivity Profiling." SLAS TECHNOLOGY: Translating Life Sciences Innovation 22, no. 2 (November 15, 2016): 153–62. http://dx.doi.org/10.1177/2211068216677248.
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Kinase profiling during drug discovery is a necessary process to confirm inhibitor selectivity and assess off-target activities. However, cost and logistical limitations prevent profiling activities from being performed in-house. We describe the development of an automated and flexible kinase profiling workflow that combines ready-to-use kinase enzymes and substrates in convenient eight-tube strips, a bench-top liquid handling device, ADP-Glo Kinase Assay (Promega, Madison, WI) technology to quantify enzyme activity, and a multimode detection instrument. Automated methods were developed for kinase reactions and quantification reactions to be assembled on a Gilson (Middleton, WI) PIPETMAX, following standardized plate layouts for single- and multidose compound profiling. Pipetting protocols were customized at runtime based on user-provided information, including compound number, increment for compound titrations, and number of kinase families to use. After the automated liquid handling procedures, a GloMax Discover (Promega) microplate reader preloaded with SMART protocols was used for luminescence detection and automatic data analysis. The functionality of the automated workflow was evaluated with several compound-kinase combinations in single-dose or dose-response profiling formats. Known target-specific inhibitions were confirmed. Novel small molecule-kinase interactions, including off-target inhibitions, were identified and confirmed in secondary studies. By adopting this streamlined profiling process, researchers can quickly and efficiently profile compounds of interest on site.
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Kamel Ghalibaf, Azadeh, Zahra Mazloum Khorasani, Mahdi Gholian Aval, and Mahmood Tara. "Aspects of User Profiling in Computer-based Health Information Tailoring Systems: A Narrative Review." Medical Technologies Journal 1, no. 4 (November 29, 2017): 105–6. http://dx.doi.org/10.26415/2572-004x-vol1iss4p105-106.
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Introduction:
The recent shift from the conventional physician-centered approach to the more polpular approach that with the focuse on patient as the center of healthcare, emphaizes on the critical role of informing and educating patients. Studies shown that tailoring health information to the needs of individuals is more effective than generic materials. Recent improvements in the fields of computer science and Information Communication Technology have made it possible to computerize such an adaptation process. Information tailoring systems use an internal representation of user conditions and needs, which is referred to as a “user model” or “user profile.”
A user profile represents the system’s beliefs about the user. Hence, it may simply contain demographic information or sophisticated factors such as the state of the disease, user’s attitude, interest, preference, and knowledge. The user profile is known as the basis for designing other system components and has a great impact on the acceptance of the system by the user and the quality of the tailored information.
To the best of our knowledge, no studies have been conducted so far to analyze and classify user profile aspects and characteristics. In this systematic narrative review, we aim to provide aspects of profiling in health information tailoring systems based on literature from different disciplines.
Methods:
comprehensive searches of the PubMed and Scopus databases have been conducted. We searched among English papers with publishing dates ranging from 1990 onward; since that is when computer-tailoring first appeared within the literature. we have devised a list of terms pertinent to the main concepts of computer-tailoring and used a qualitative–interpretive approach for data extraction.
Results:
Analyzing the data from 32 eligible studies, we found three aspects in designing a tailoring user profile. Each aspect with its characteristics are provided below:
1-Identifying common factors used in profiles and classifying these factors thematically, which has three attributes:
The number of factors used to design the user profile and their diversity (e.g. demographic,clinical,behavioral information, learning style and so forth)
The approaches used to Identify effective factors in tailoring (e.g. evidence-based, avalible data sources)
Attributes of the factors (e.g. long-term/short-term, static/dynamic)
2-Data collection tools and methods, which has two attributes:
Data collection methods (e.g. explicit, implicit, mixed)
Assessment tool (e.g. questionnaire, patient record)
3-Data interpretation that demonstrates to what extent the collected data needs to be analyzed to use in tailoring.
we have also identified two main approaches regarding tailoring: public health and computational tailoring. Public Health communication researcher has relied greatly on health behavior models but generally has used simpler technological approaches, whereas computer science employed more advanced technological approaches but integrated behavior theory to a lesser extent. These two approaches complete each other to provide the necessary requirements for designing a practical tailoring system in future studies.
Conclusion: In this study we investigate different aspects of designing a user profile in health information tailoring systems. The proposed model is a valuable guide for new researchers in the field. Results from this review provide a comprehensive overview of the field and will help researchers to combine effective methods from across the disciplines in future research.
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Li, Lei. "Learning Recommendation Algorithm Based on Improved BP Neural Network in Music Marketing Strategy." Computational Intelligence and Neuroscience 2021 (November 30, 2021): 1–10. http://dx.doi.org/10.1155/2021/2073881.
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The growth and popularity of streaming music have changed the way people consume music, and users can listen to online music anytime and anywhere. By integrating various recommendation algorithms/strategies (user profiling, collaborative filtering, content filtering, etc.), we capture users’ interests and preferences and recommend the content of interest to them. To address the sparsity of behavioral data in digital music marketing, which leads to inadequate mining of user music preference features, a metric ranking learning recommendation algorithm with fused content representation is proposed. Relative partial order relations are constructed using observed and unobserved behavioral data to enable the model to be fully trained, while audio feature extraction submodels related to the recommendation task are constructed to further alleviate the data sparsity problem, and finally, the preference relationships between users and songs are mined through metric learning. Convolutional neural networks are used to extract the high-level semantic features of songs, and then the high-level semantic features of songs extracted from the previous layer are reformed into a session time sequence list according to the time sequence of user listening in order to build a bidirectional recurrent neural network model based on the attention mechanism so that it can reduce the influence of noisy data and learn the strong dependencies between songs.
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Basarudin, Noor Ashikin, and Ridwan Adetunji Raji. "Implication of Personalized Advertising on Personal Data: A Legal Analysis of the EU General Data Protection Regulation." Environment-Behaviour Proceedings Journal 7, no. 22 (November 30, 2022): 109–14. http://dx.doi.org/10.21834/ebpj.v7i22.4160.
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The accelerating emergence of personalised advertising is mostly driven by data. Accordingly, algorithmic profiling has become a constant experience for every online user in predicting preference and interest. The profiling process raises several issues of human privacy and personal data invasion. Therefore, this study adopts the doctrinal legal method through the analysis of International Instruments and the European Union General Data Protection Regulation as legal avenue to safeguard and protect online activities of the data subjects. The findings of this paper discuss the main principles to be observed by the data controller in ensuring the legality of personal data profiling. This paper suggests the profiling process to be design-based security due to unavailability of system procedure to human knowledge. Keywords: Personalised Advertising; Algorithmic Targeting; Personal Data Profiling; EU General Data Protection Regulation eISSN: 2398-4287 © 2022. The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians/Africans/Arabians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, Malaysia. DOI: https://doi.org/10.21834/ebpj.v7i22.4160
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Breja, Manvi. "Prediction of the Next Question for the Question Answering System." International Journal of Informatics and Communication Technology (IJ-ICT) 5, no. 2 (August 1, 2016): 51. http://dx.doi.org/10.11591/ijict.v5i2.pp51-59.
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<span>User profiling, one of the main issue faced while implementing the efficient question answering system, in which the user profile is made, containing the data posed by the user, capturing their domain of interest. The paper presents the method of predicting the next related questions to the first initial question provided by the user to the question answering search engine. A novel approach of the association rule mining is highlighted in which the information is extracted from the log of the previously submitted questions to the question answering search engine, using algorithms for mining association rules and predicts the set of next questions that the user will provide to the system in the next session. Using this approach, the question answering system keeps the relevant answers of the next questions in the repository for providing a speedy response to the user and thus increasing the efficiency of the system.</span>
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Huang, Weizhi, Wenkai Mo, Beijun Shen, Yu Yang, and Ning Li. "Automatically Modeling Developer Programming Ability and Interest Across Software Communities." International Journal of Software Engineering and Knowledge Engineering 26, no. 09n10 (November 2016): 1493–510. http://dx.doi.org/10.1142/s0218194016400143.
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Developer profile plays an important role in software project planning, developer recommendation, personnel training, and other tasks. Modeling the ability and interest of developers is its key issue. However, most existing approaches require manual assessment, like 360[Formula: see text] performance evaluation. With the emergence of social networking sites such as StackOverflow and Github, a vast amount of developer information is created on a daily basis. Such personal and social context data has huge potential to support automatic and effective developer ability evaluation and interest mining. In this paper, we propose CPDScorer, a novel approach for modeling and scoring the programming ability and interest of developers through mining heterogeneous information from both community question answering (CQA) sites and open-source software (OSS) communities. CPDScorer analyzes the questions and answers posted in CQA sites, and evaluates the projects submitted in OSS communities to assign expertise scores as well as interest scores to developers, considering both the quantitative and qualitative factors. When profiling developer's ability and interest, a programming term extraction algorithm is also designed based on set covering. We have conducted experiments on StackOverflow and Github to measure the effectiveness of CPDScorer. The results show that our approach is feasible and practical in user programming ability and interest modeling. In particular, the precision of our approach reaches 80%.
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Voulgaridou, Georgia-Persephoni, Theodora Mantso, Ioannis Anestopoulos, Ariel Klavaris, Christina Katzastra, Despoina-Eugenia Kiousi, Marini Mantela, et al. "Toxicity Profiling of Biosurfactants Produced by Novel Marine Bacterial Strains." International Journal of Molecular Sciences 22, no. 5 (February 27, 2021): 2383. http://dx.doi.org/10.3390/ijms22052383.
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Surface active agents (SAAs), currently used in modern industry, are synthetic chemicals produced from non-renewable sources, with potential toxic impacts on humans and the environment. Thus, there is an increased interest for the identification and utilization of natural derived SAAs. As such, the marine environment is considered a promising source of biosurfactants with low toxicity, environmental compatibility, and biodegradation compared to their synthetic counterparts. MARISURF is a Horizon 2020 EU-funded project aiming to identify and functionally characterize SAAs, derived from a unique marine bacterial collection, towards commercial exploitation. Specifically, rhamnolipids produced by Marinobacter MCTG107b and Pseudomonas MCTG214(3b1) strains were previously identified and characterized while currently their toxicity profile was assessed by utilizing well-established methodologies. Our results showed a lack of cytotoxicity in in vitro models of human skin and liver as indicated by alamar blue and propidium iodide assays. Additionally, the use of the single gel electrophoresis assay, under oxidative stress conditions, revealed absence of any significant mutagenic/anti-mutagenic potential. Finally, both 2,2’-azino-bis (3-ethylbenzothiazoline-6-sulphonicacid) (ABTS) and 2,2-diphenyl-1-picrylhydrazyl radical (DPPH) cell-free assays, revealed no significant anti-oxidant capacity for neither of the tested compounds. Consequently, the absence of significant cytotoxicity and/or mutagenicity justifies their commercial exploitation and potential development into industrial end-user applications as natural and environmentally friendly biosurfactants.
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Orama, Jonathan Ayebakuro, Joan Borràs, and Antonio Moreno. "Combining Cluster-Based Profiling Based on Social Media Features and Association Rule Mining for Personalised Recommendations of Touristic Activities." Applied Sciences 11, no. 14 (July 15, 2021): 6512. http://dx.doi.org/10.3390/app11146512.
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Tourists who visit a city for the first time may find it difficult to decide on places to visit, as the amount of information in the Web about cultural and leisure activities may be large. Recommender systems address this problem by suggesting the points of interest that fit better with the user’s preferences. This paper presents a novel recommender system that leverages tweets to build user profiles, taking into account not only their personal preferences but also their travel habits. Association rules, which are mined from the previous visits of users documented on Twitter, are used to make the final recommendations of places to visit. The system has been applied to data of the city of Barcelona, and the results show that the use of the social media-based clustering procedure increases its performance according to several relevant metrics.
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Konstantakis, Markos, Yannis Christodoulou, Georgios Alexandridis, Alexandros Teneketzis, and George Caridakis. "ACUX Typology: A Harmonisation of Cultural-Visitor Typologies for Multi-Profile Classification." Digital 2, no. 3 (June 24, 2022): 365–78. http://dx.doi.org/10.3390/digital2030020.
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The modern cultural industry and the related academic sectors have shown increased interest in Cultural User eXperience (CUX) research, since it constitutes a critical factor to examine and apply when presenting cultural content. Recent CUX studies show that visitors tend to carry their own cultural characteristics and preferences when visiting destinations of cultural interest, thus obtaining a virtually unique experience. To cope with this tendency, various research efforts have been made to identify different profiles of cultural visitors based on their background and preferences and classify them into distinct visitor types. In this paper, we proposed the ACUX (Augmented Cultural User eXperience) typology for classifying visitors of cultural destinations. The proposed typology aims to provide the multi-profile classification of cultural visitors based on their visiting preferences. Methodology-wise, the ACUX typology was the output of a harmonisation process of existing cultural-visitor typologies that base their classification on visiting preferences. The proposed typology was evaluated in juxtaposition with the harmonised typologies from which it was derived through an experiment conducted using a recommender and a dataset of TripAdvisor user responses. The evaluation showed that the ACUX typology achieved a more accurate profiling of cultural visitors, enabling them to reduce information overload by directly suggesting content that is more likely to meet their diverse preferences and needs.
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Paulson, Thomas, and Victoria Goosey-Tolfrey. "Current Perspectives on Profiling and Enhancing Wheelchair Court Sport Performance." International Journal of Sports Physiology and Performance 12, no. 3 (March 2017): 275–86. http://dx.doi.org/10.1123/ijspp.2016-0231.
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Despite the growing interest in Paralympic sport, the evidence base for supporting elite wheelchair sport performance remains in its infancy when compared with able-bodied (AB) sport. Subsequently, current practice is often based on theory adapted from AB guidelines, with a heavy reliance on anecdotal evidence and practitioner experience. Many principles in training prescription and performance monitoring with wheelchair athletes are directly transferable from AB practice, including the periodization and tapering of athlete loads around competition, yet considerations for the physiological consequences of an athlete’s impairment and the interface between athlete and equipment are vital when targeting interventions to optimize in-competition performance. Researchers and practitioners are faced with the challenge of identifying and implementing reliable protocols that detect small but meaningful changes in impairment-specific physical capacities and on-court performance. Technologies to profile both linear and rotational on-court performance are an essential component of sport-science support to understand sport-specific movement profiles and prescribe training intensities. In addition, an individualized approach to the prescription of athlete training and optimization of the “wheelchair–user interface” is required, accounting for an athlete’s anthropometrics, sports classification, and positional role on court. In addition to enhancing physical capacities, interventions must focus on the integration of the athlete and his or her equipment, as well as techniques for limiting environmental influence on performance. Taken together, the optimization of wheelchair sport performance requires a multidisciplinary approach based on the individual requirements of each athlete.
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Meißner, Tobias, Anja Seckinger, Thierry Rème, Thomas Hielscher, Thomas Möhler, Kai Neben, Hartmut Goldschmidt, Bernard Klein, and Dirk Hose. "Metascoring and Gene Expression Profiling in Clinical Routine in Multiple Myeloma,." Blood 118, no. 21 (November 18, 2011): 3940. http://dx.doi.org/10.1182/blood.v118.21.3940.3940.
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Abstract Abstract 3940 BACKGROUND. Multiple myeloma is characterized by molecular heterogeneity transmitting to survival ranging from several months to over 15 years. Gene expression profiling allows assessment of biological entities, risk, and targets. Its translation into clinical routine alongside conventional prognostic factors has been prevented by lack of appropriated reporting tools and the integration with other prognostic factors into a single risk stratification (metascoring). METHODS. We present here a non-commercial open source software-framework developed in the open source language R (GEP-report) containing a graphic user interphase based on Gtk2. Affymetrix microarray raw-data and a documentation-by-value strategy to directly apply thresholds and grouping-algorithms from a reference cohort of 262 myeloma patients are used. Gene expression-based and conventional prognostic factors are integrated within one risk-stratification (HM-metascore) and the final report is created as a PDF-file. RESULTS. The GEP-report comprises i) quality control, ii) test of sample identity, iii) biological classifications of multiple myeloma, iv) risk stratification, v) assessment of target-genes, and vi) integration of expression-based and clinical risk factors within one metascore. This HM-metascore sums over the weighted factors gene-expression based risk-assessment (UAMS-, IFM-score), proliferation, ISS-stage, t(4;14), and expression of prognostic target-genes (AURKA, IGF1R) for which clinical grade inhibitors exist. It delineates three significantly different groups of 13.1, 72.1 and 14.7% of patients with a 6-year survival of 89.3, 60.6 and 18.6%, respectively. CONCLUSION. GEP-reporting allows prospective assessment of target gene expression and integration of current prognostic factors within one risk stratification (metascoring), being customizable regarding novel parameters or other cancer entities. Disclosures: No relevant conflicts of interest to declare.
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Dutta, Nirmita, Peter B. Lillehoj, Pedro Estrela, and Gorachand Dutta. "Electrochemical Biosensors for Cytokine Profiling: Recent Advancements and Possibilities in the Near Future." Biosensors 11, no. 3 (March 23, 2021): 94. http://dx.doi.org/10.3390/bios11030094.
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Cytokines are soluble proteins secreted by immune cells that act as molecular messengers relaying instructions and mediating various functions performed by the cellular counterparts of the immune system, by means of a synchronized cascade of signaling pathways. Aberrant expression of cytokines can be indicative of anomalous behavior of the immunoregulatory system, as seen in various illnesses and conditions, such as cancer, autoimmunity, neurodegeneration and other physiological disorders. Cancer and autoimmune diseases are particularly adept at developing mechanisms to escape and modulate the immune system checkpoints, reflected by an altered cytokine profile. Cytokine profiling can provide valuable information for diagnosing such diseases and monitoring their progression, as well as assessing the efficacy of immunotherapeutic regiments. Toward this goal, there has been immense interest in the development of ultrasensitive quantitative detection techniques for cytokines, which involves technologies from various scientific disciplines, such as immunology, electrochemistry, photometry, nanotechnology and electronics. This review focusses on one aspect of this collective effort: electrochemical biosensors. Among the various types of biosensors available, electrochemical biosensors are one of the most reliable, user-friendly, easy to manufacture, cost-effective and versatile technologies that can yield results within a short period of time, making it extremely promising for routine clinical testing.
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Chen, Yi-An, Jonguk Park, Yayoi Natsume-Kitatani, Hitoshi Kawashima, Attayeb Mohsen, Koji Hosomi, Kumpei Tanisawa, et al. "MANTA, an integrative database and analysis platform that relates microbiome and phenotypic data." PLOS ONE 15, no. 12 (December 4, 2020): e0243609. http://dx.doi.org/10.1371/journal.pone.0243609.
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With an ever-increasing interest in understanding the relationships between the microbiota and the host, more tools to map, analyze and interpret these relationships have been developed. Most of these tools, however, focus on taxonomic profiling and comparative analysis among groups, with very few analytical tools designed to correlate microbiota and the host phenotypic data. We have developed a software program for creating a web-based integrative database and analysis platform called MANTA (Microbiota And pheNoType correlation Analysis platform). In addition to storing the data, MANTA is equipped with an intuitive user interface that can be used to correlate the microbial composition with phenotypic parameters. Using a case study, we demonstrated that MANTA was able to quickly identify the significant correlations between microbial abundances and phenotypes that are supported by previous studies. Moreover, MANTA enabled the users to quick access locally stored data that can help interpret microbiota-phenotype relations. MANTA is available at https://mizuguchilab.org/manta/ for download and the source code can be found at https://github.com/chenyian-nibio/manta.
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Crivellari, Alessandro, and Euro Beinat. "Identifying Foreign Tourists’ Nationality from Mobility Traces via LSTM Neural Network and Location Embeddings." Applied Sciences 9, no. 14 (July 18, 2019): 2861. http://dx.doi.org/10.3390/app9142861.
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The interest in human mobility analysis has increased with the rapid growth of positioning technology and motion tracking, leading to a variety of studies based on trajectory recordings. Mapping the routes that people commonly perform was revealed to be very useful for location-based service applications, where individual mobility behaviors can potentially disclose meaningful information about each customer and be fruitfully used for personalized recommendation systems. This paper tackles a novel trajectory labeling problem related to the context of user profiling in “smart” tourism, inferring the nationality of individual users on the basis of their motion trajectories. In particular, we use large-scale motion traces of short-term foreign visitors as a way of detecting the nationality of individuals. This task is not trivial, relying on the hypothesis that foreign tourists of different nationalities may not only visit different locations, but also move in a different way between the same locations. The problem is defined as a multinomial classification with a few tens of classes (nationalities) and sparse location-based trajectory data. We hereby propose a machine learning-based methodology, consisting of a long short-term memory (LSTM) neural network trained on vector representations of locations, in order to capture the underlying semantics of user mobility patterns. Experiments conducted on a real-world big dataset demonstrate that our method achieves considerably higher performances than baseline and traditional approaches.
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Segal, Neil H., Paul Pavlidis, William S. Noble, Cristina R. Antonescu, Agnes Viale, Umadevi V. Wesley, Klaus Busam, et al. "Classification of Clear-Cell Sarcoma as a Subtype of Melanoma by Genomic Profiling." Journal of Clinical Oncology 21, no. 9 (May 1, 2003): 1775–81. http://dx.doi.org/10.1200/jco.2003.10.108.
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Purpose: To develop a genome-based classification scheme for clear-cell sarcoma (CCS), also known as melanoma of soft parts (MSP), which would have implications for diagnosis and treatment. This tumor displays characteristic features of soft tissue sarcoma (STS), including deep soft tissue primary location and a characteristic translocation, t(12;22)(q13;q12), involving EWS and ATF1 genes. CCS/MSP also has typical melanoma features, including immunoreactivity for S100 and HMB45, pigmentation, MITF-M expression, and a propensity for regional lymph node metastases. Materials and Methods: RNA samples from 21 cell lines and 60 pathologically confirmed cases of STS, melanoma, and CCS/MSP were examined using the U95A GeneChip (Affymetrix, Santa Clara, CA). Hierarchical cluster analysis, principal component analysis, and support vector machine (SVM) analysis exploited genomic correlations within the data to classify CCS/MSP. Results: Unsupervised analyses demonstrated a clear distinction between STS and melanoma and, furthermore, showed that CCS/MSP cluster with the melanomas as a distinct group. A supervised SVM learning approach further validated this finding and provided a user-independent approach to diagnosis. Genes of interest that discriminate CCS/MSP included those encoding melanocyte differentiation antigens, MITF, SOX10, ERBB3, and FGFR1. Conclusion: Gene expression profiles support the classification of CCS/MSP as a distinct genomic subtype of melanoma. Analysis of these gene profiles using the SVM may be an important diagnostic tool. Genomic analysis identified potential targets for the development of therapeutic strategies in the treatment of this disease.
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Ru, Beibei, Ching Ngar Wong, Yin Tong, Jia Yi Zhong, Sophia Shek Wa Zhong, Wai Chung Wu, Ka Chi Chu, et al. "TISIDB: an integrated repository portal for tumor–immune system interactions." Bioinformatics 35, no. 20 (March 23, 2019): 4200–4202. http://dx.doi.org/10.1093/bioinformatics/btz210.
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Abstract Summary The interaction between tumor and immune system plays a crucial role in both cancer development and treatment response. To facilitate comprehensive investigation of tumor–immune interactions, we have designed a user-friendly web portal TISIDB, which integrated multiple types of data resources in oncoimmunology. First, we manually curated 4176 records from 2530 publications, which reported 988 genes related to anti-tumor immunity. Second, genes associated with the resistance or sensitivity of tumor cells to T cell-mediated killing and immunotherapy were identified by analyzing high-throughput screening and genomic profiling data. Third, associations between any gene and immune features, such as lymphocytes, immunomodulators and chemokines, were pre-calculated for 30 TCGA cancer types. In TISIDB, biologists can cross-check a gene of interest about its role in tumor–immune interactions through literature mining and high-throughput data analysis, and generate testable hypotheses and high quality figures for publication. Availability and implementation http://cis.hku.hk/TISIDB Supplementary information Supplementary data are available at Bioinformatics online.
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Boutsi, A. M., C. Ioannidis, and S. Soile. "HYBRID MOBILE AUGMENTED REALITY: WEB-LIKE CONCEPTS APPLIED TO HIGH RESOLUTION 3D OVERLAYS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W17 (November 29, 2019): 85–92. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w17-85-2019.
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Abstract. Mobile Augmented Reality (MAR) aligns toward current technological advances with more intuitive interfaces, realistic graphic content and flexible development processes. The case of overlaying precise 3D representations exploits their high penetration to induct users to a world where data are perceived as real counterparts. The work presented in this paper integrates web-like concepts with hybrid mobile tools to visualize high-quality and complex 3D geometry on the real environment. The implementation involves two different operational mechanisms: anchors and location-sensitive tracking. Three scenarios, for indoors and outdoors are developed using open-source and with no limit on distribution SDKs, APIs and rendering engines. The JavaScript-driven prototype consolidates some of the overarching principles of AR, such as pose estimation, registration and 3D tracking to an interactive User Interface under the scene graph concept. The 3D overlays are shown to the end user i) on top of an image target ii) on real-world planar surfaces and iii) at predefined points of interest (POI). The evaluation in terms of performance, rendering efficacy and responsiveness is made through various testing strategies: system and trace logs, profiling and ‗end-to-end‖ tests. The final benchmarking elucidates the slow and computationally intensive procedures induced by the big data rendering and optimization patterns are proposed to mitigate the performance impact to the non-native technologies.
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Gerolami, Justin, Justin Jong Mun Wong, Ricky Zhang, Tong Chen, Tashifa Imtiaz, Miranda Smith, Tamara Jamaspishvili, et al. "A Computational Approach to Identification of Candidate Biomarkers in High-Dimensional Molecular Data." Diagnostics 12, no. 8 (August 18, 2022): 1997. http://dx.doi.org/10.3390/diagnostics12081997.
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Complex high-dimensional datasets that are challenging to analyze are frequently produced through ‘-omics’ profiling. Typically, these datasets contain more genomic features than samples, limiting the use of multivariable statistical and machine learning-based approaches to analysis. Therefore, effective alternative approaches are urgently needed to identify features-of-interest in ‘-omics’ data. In this study, we present the molecular feature selection tool, a novel, ensemble-based, feature selection application for identifying candidate biomarkers in ‘-omics’ data. As proof-of-principle, we applied the molecular feature selection tool to identify a small set of immune-related genes as potential biomarkers of three prostate adenocarcinoma subtypes. Furthermore, we tested the selected genes in a model to classify the three subtypes and compared the results to models built using all genes and all differentially expressed genes. Genes identified with the molecular feature selection tool performed better than the other models in this study in all comparison metrics: accuracy, precision, recall, and F1-score using a significantly smaller set of genes. In addition, we developed a simple graphical user interface for the molecular feature selection tool, which is available for free download. This user-friendly interface is a valuable tool for the identification of potential biomarkers in gene expression datasets and is an asset for biomarker discovery studies.
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Silem, Abd El Heq, Hajer Taktak, and Faouzi Moussa. "User interests profiling using fuzzy regression tree." International Journal of Ad Hoc and Ubiquitous Computing 41, no. 3 (2022): 191. http://dx.doi.org/10.1504/ijahuc.2022.10050035.
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Silem, Abd El Heq, Hajer Taktak, and Faouzi Moussa. "User interests profiling using fuzzy regression tree." International Journal of Ad Hoc and Ubiquitous Computing 41, no. 3 (2022): 191. http://dx.doi.org/10.1504/ijahuc.2022.126114.
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Schmidt, Jane, Paige Johnson, and Michael Anderson. "Multiplex measurement of inflammatory cytokines in human plasma using a Fluorokine® MAP multiplex assay (124.5)." Journal of Immunology 188, no. 1_Supplement (May 1, 2012): 124.5. http://dx.doi.org/10.4049/jimmunol.188.supp.124.5.
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Abstract Fluorokine® Multianalyte Profiling (MAP) Kits offer the ability to simultaneously measure multiple analytes with less operator time, less sample consumption, and lower cost per result compared to conventional ELISAs. To demonstrate this, we cultured whole blood samples from apparently healthy donors for up to 24 hours without stimulation or stimulated by LPS, PHA, or PMA. Samples were taken from the cultures and processed into plasma. GM-CSF, IFN-γ, IL-1β, IL-2, IL-4, IL-5, IL-6, IL-8, IL-10, IL-12, TNF-α, and VEGF were quantified simultaneously using the Human Fluorokine MAP High Sensitivity Cytokine Panel to compare cytokine profiles in stimulated and unstimulated cultures. The Human Fluorokine MAP High Sensitivity Cytokine Panel is a fully validated multiplex assay designed to measure up to twelve inflammation biomarkers in serum or plasma; the specific analytes are selected by the user. Analyte-specific antibodies pre-coated onto color-coded microparticles capture the analytes of interest in standards or samples. After washing away any unbound substances in a filter-bottom microplate, a cocktail of biotinylated antibodies also specific to the analytes of interest is added to each well, followed by a streptavidin-phycoerythrin conjugate. Following a final wash, microparticles are resuspended in buffer and read using a Luminex®-based analyzer. The assay is fully validated for accurate, precise, and reproducible results using serum or plasma samples.
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Chen, Enhong, Guangxiang Zeng, Ping Luo, Hengshu Zhu, Jilei Tian, and Hui Xiong. "Discerning individual interests and shared interests for social user profiling." World Wide Web 20, no. 2 (June 14, 2016): 417–35. http://dx.doi.org/10.1007/s11280-016-0397-x.
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Gu, Won, Jeongsup Moon, Crispen Chisina, Byungkon Kang, Taesung Park, and Hyunwook Koh. "MiCloud: A unified web platform for comprehensive microbiome data analysis." PLOS ONE 17, no. 8 (August 1, 2022): e0272354. http://dx.doi.org/10.1371/journal.pone.0272354.
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The recent advance in massively parallel sequencing has enabled accurate microbiome profiling at a dramatically lowered cost. Then, the human microbiome has been the subject of intensive investigation in public health and medicine. In the meanwhile, researchers have developed lots of microbiome data analysis methods, protocols, and/or tools. Among those, especially, the web platforms can be highlighted because of the user-friendly interfaces and streamlined protocols for a long sequence of analytic procedures. However, existing web platforms can handle only a categorical trait of interest, cross-sectional study design, and the analysis with no covariate adjustment. We therefore introduce here a unified web platform, named MiCloud, for a binary or continuous trait of interest, cross-sectional or longitudinal/family-based study design, and with or without covariate adjustment. MiCloud handles all such types of analyses for both ecological measures (i.e., alpha and beta diversity indices) and microbial taxa in relative abundance on different taxonomic levels (i.e., phylum, class, order, family, genus and species). Importantly, MiCloud also provides a unified analytic protocol that streamlines data inputs, quality controls, data transformations, statistical methods and visualizations with vastly extended utility and flexibility that are suited to microbiome data analysis. We illustrate the use of MiCloud through the United Kingdom twin study on the association between gut microbiome and body mass index adjusting for age. MiCloud can be implemented on either the web server (http://micloud.kr) or the user’s computer (https://github.com/wg99526/micloudgit).
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Nicoletti, Matias, Silvia Schiaffino, and Daniela Godoy. "Mining interests for user profiling in electronic conversations." Expert Systems with Applications 40, no. 2 (February 2013): 638–45. http://dx.doi.org/10.1016/j.eswa.2012.07.075.
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Badr, Ameer A., and Alia K. Abdul Hassan. "Age Estimation in Short Speech Utterances Based on Bidirectional Gated-Recurrent Neural Networks." Engineering and Technology Journal 39, no. 1B (March 25, 2021): 129–40. http://dx.doi.org/10.30684/etj.v39i1b.1905.
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Recently, age estimates from speech have received growing interest as they are important for many applications like custom call routing, targeted marketing, or user-profiling. In this work, an automatic system to estimate age in short speech utterances without depending on the text is proposed. From each utterance frame, four groups of features are extracted and then 10 statistical functionals are measured for each extracted dimension of the features, to be followed by dimensionality reduction using Linear Discriminant Analysis (LDA). Finally, bidirectional Gated-Recurrent Neural Networks (G-RNNs) are used to predict speaker age. Experiments are conducted on the VoxCeleb1 dataset to show the performance of the proposed system, which is the first attempt to do so for such a system. In gender-dependent system, the Mean Absolute Error (MAE) of the proposed system is 9.25 years, and 10.33 years, the Root Mean Square Error (RMSE) is 13.17 and 13.26, respectively, for female and male speakers. In gender_ independent system, the MAE of the proposed system is 10.96 years, and the RMSE is 15.47. The results show that the proposed system has a good performance on short-duration utterances, taking into consideration the high noise ratio in the VoxCeleb1 dataset.
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Grentzinger, Thomas, Stefan Oberlin, Gregory Schott, Dominik Handler, Julia Svozil, Veronica Barragan-Borrero, Adeline Humbert, Sandra Duharcourt, Julius Brennecke, and Olivier Voinnet. "A universal method for the rapid isolation of all known classes of functional silencing small RNAs." Nucleic Acids Research 48, no. 14 (June 4, 2020): e79-e79. http://dx.doi.org/10.1093/nar/gkaa472.
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Abstract Diverse classes of silencing small (s)RNAs operate via ARGONAUTE-family proteins within RNA-induced-silencing-complexes (RISCs). Here, we have streamlined various embodiments of a Q-sepharose-based RISC-purification method that relies on conserved biochemical properties of all ARGONAUTEs. We show, in multiple benchmarking assays, that the resulting 15-min benchtop extraction procedure allows simultaneous purification of all known classes of RISC-associated sRNAs without prior knowledge of the samples-intrinsic ARGONAUTE repertoires. Optimized under a user-friendly format, the method – coined ‘TraPR’ for Trans-kingdom, rapid, affordable Purification of RISCs – operates irrespectively of the organism, tissue, cell type or bio-fluid of interest, and scales to minute amounts of input material. The method is highly suited for direct profiling of silencing sRNAs, with TraPR-generated sequencing libraries outperforming those obtained via gold-standard procedures that require immunoprecipitations and/or lengthy polyacrylamide gel-selection. TraPR considerably improves the quality and consistency of silencing sRNA sample preparation including from notoriously difficult-to-handle tissues/bio-fluids such as starchy storage roots or mammalian plasma, and regardless of RNA contaminants or RNA degradation status of samples.
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Finkelstein, David B., James Madson, Lucian Vacaroiu, Alexander M. Gout, Leonard Hirja, Stephanie Sandor, Andrew Thrasher, et al. "Abstract 1901: VisComm: A cloud-based platform for accessing and creating scientific visualization for the pediatric cancer research community." Cancer Research 82, no. 12_Supplement (June 15, 2022): 1901. http://dx.doi.org/10.1158/1538-7445.am2022-1901.
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Abstract Scientific visualization is important to cancer research as different graphs enable researchers to spot trends and detect outliers in large-scale, high-dimensional data. For example, mutation landscape maps integrate mutational and clinical data, revealing which somatic alterations are most closely associated with diagnoses, age, ethnicity, and outcome; t-SNE plots allow dissection of cancer subtypes based on gene expression or methylation profiling. These graphs are often created by researchers after extensive data curation and optimization of the layout using software tools designed for data visualization and are commonly presented as static figures in published literature. To permit seamless access to the genomic and epigenomic visualization tools created at St. Jude, we developed Visualization Community (https://viz.stjude.cloud/, VisComm), which allows researchers to explore published graphs, develop custom visualization for their private data, and also serve as a public repository for completed graphs, as part of the St. Jude Cloud data sharing ecosystem. Sixty-five (65) interactive graphs from 31 publications in 231 pediatric subtypes are currently available, including t-SNE plots of RNA-seq and methylation data, mutation landscape maps, variant visualization on protein or genomic axes, epigenome states, and chromatin contact interactions. The graphs are searchable by cancer diagnosis, molecular profiling, research interest, publication, and research organization with features designed to enable user customization. To date, 5,014 users, 25% of whom are recurrent, have accessed VisComm. On average, each public visualization has over 4,000 views while the most accessed graph has >13,000 views. We have developed the ability to work in teams with in VisComm to empower collaborative development of new visualizations and controlled release by a research team. This feature enables the professional quality visualizations serve as resources for focused scientific communities such as the Audacious Goals Initiative (AGI) - Retina), a multi-institutional cross-disciplinary research team whose goal is restoring vision to patients by regenerating retinas. Videos and textual tutorials are being prepared using data generated from programs such as the Genome for Kids (G4K), a clinical genomic profiling program, that will help guide users to apply the underlying visualization software tools and to integrate public and private data of all types. VisComm is the first platform dedicated to enabling the development and sharing of interactive high-quality data-rich visualizations, which we expect will enhance data exploration and hypothesis generation for cancer research. Citation Format: David B. Finkelstein, James Madson, Lucian Vacaroiu, Alexander M. Gout, Leonard Hirja, Stephanie Sandor, Andrew Thrasher, Colleen Reilly, Jian Wang, Xiaolong Chen, Xin Zhou, Jinghui Zhang, Clay McLeod. VisComm: A cloud-based platform for accessing and creating scientific visualization for the pediatric cancer research community [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1901.
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Gage, Thomas, Ali Kazeroonian, and German A. Pihan. "Genome Redux for Hematologists: a Graphical User Interface for Visualizing and Reducing Genome-Wide Data Sets Into Clinically Actionable Information." Blood 116, no. 21 (November 19, 2010): 2558. http://dx.doi.org/10.1182/blood.v116.21.2558.2558.
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Abstract Abstract 2558 Background: The increasing use in the clinic of genome-wide analyses has placed a greater burden on, and need for novel informatics tools. Ideally such tools should be capable of reducing the vast amount of information generated per patient into integrated, simplified, intuitive, and preferably, visual data that can be easily translated into diagnostic, prognostic and theranostic actionable information. With the overarching goal of clarity and user-friendliness in mind we have co-opted a genome-wide representational tool used widely in non-clinical discovery genomics and tailored it to achieve its intended clinical use, i.e. simple graphical representation of complex data enabling clinicians to quickly derive meaning from robust clinical laboratory modalities, such as next-generation sequencing technologies and next-gen microarrays. Materials & Methods: For a proof of concept rendition, we collected cytogenetics, array CGH, and gene expression data on a subset of well-characterized core binding factor positive leukemias. Leukemias containing CBFA2 [t(8;21)(q22;q22)] or CBFB [inv(16)(p13q22)] fusion proteins were included in the study. Gene expression profiling data sets were extracted from Stanford Microarray Database. The associated karyotypes were obtained from the linked PubMed papers and directly from the authors. aCGH data sets were extracted from the SKY/M-FISH and CGH database at NCBI. All datasets for each case of CBF+ leukemia represented in this study were unified into a Microsoft Access Database, which contained the numeric coordinates of all genes included, and their associated cytogenetic band position. Results: By subjecting the data to customized subroutines, it was possible to extract and display relevant subsets of data into a customizable visually intuitive display, which allowed naïve observers to quickly assimilate all the clinically relevant genetic information on a particular AML case. From such a representation, heat maps of subsets of genes, structural and numerical chromosome abnormalities, copy-number changes and subsets of relevant point mutations could be displayed, all in a single integrated genome anchored image. Discussion: Our graphic user interface displays positionally-anchored genome-wide data and could be customized to represent CNVs, miRNA expression and DNA methylation patterns, associated phenotypes, etc, in addition to those shown in this study. Furthermore, any of these parameters can be segmented into functionally related groups to display, for instance, regulators of transcription, cell lineage or differentiation, proliferation, apoptosis, DNA repair, expression of genes that govern response or sensitivity to chemotherapy or entire signaling pathways. Conclusion: Integrated graphical representation of relevant genome-wide data facilitates and harmonizes communication among physicians with different expertise and facilitates patient stratification into defined risk groups, which is critically important in enabling risk-adapted and/or targeted therapies. Disclosures: No relevant conflicts of interest to declare.
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Cufoglu, Ayse, Mahi Lohi, and Colin Everiss. "Feature weighted clustering for user profiling." International Journal of Modeling, Simulation, and Scientific Computing 08, no. 04 (December 2017): 1750056. http://dx.doi.org/10.1142/s1793962317500568.
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Personalization is the adaptation of the services to fit the user’s interests, characteristics and needs. The key to effective personalization is user profiling. Apart from traditional collaborative and content-based approaches, a number of classification and clustering algorithms have been used to classify user related information to create user profiles. However, they are not able to achieve accurate user profiles. In this paper, we present a new clustering algorithm, namely Multi-Dimensional Clustering (MDC), to determine user profiling. The MDC is a version of the Instance-Based Learner (IBL) algorithm that assigns weights to feature values and considers these weights for the clustering. Three feature weight methods are proposed for the MDC and, all three, have been tested and evaluated. Simulations were conducted with using two sets of user profile datasets, which are the training (includes 10,000 instances) and test (includes 1000 instances) datasets. These datasets reflect each user’s personal information, preferences and interests. Additional simulations and comparisons with existing weighted and non-weighted instance-based algorithms were carried out in order to demonstrate the performance of proposed algorithm. Experimental results using the user profile datasets demonstrate that the proposed algorithm has better clustering accuracy performance compared to other algorithms. This work is based on the doctoral thesis of the corresponding author.
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Vasilkova, Valeriya, and Natalya Legostaeva. "Bots in Public Arenas of Social Networks." Sociological Journal 27, no. 4 (December 29, 2021): 99–117. http://dx.doi.org/10.19181/socjour.2021.27.4.8647.
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In the study of social bots, one of the important trends is the transition from a technology-centered understanding of bots as a threat to information and computer security to a broader, socially-focused understanding of bots as a new tool of informational influence used by various social actors in online social networks. This transition is of value to modern sociology. As one such actor, the authors consider a group of civic activists who use bot-technology to construct and solve the problem of defrauded equity holders. The novelty of the article lies in the interpretation of this group’s activities in the context of the concept of public arenas. The botnet “Deceived equity holders of LenSpecStroy” was detected thanks to the author’s complex methodology that combined the method of frequency analysis of messages, profiling of bot accounts, including static and behavioral analysis of user profiles, statistical analysis of texts, analysis of the botnet’s structural organization, analysis of the content of its publications, and analysis of bursts of network publication activity. Analyzing these bursts of publication activity and the content of botnet publications showed how bot-technologies aided in implementing effective techniques aimed at constructing and maintaining the social problem of defrauded equity holders: expanding the capacity of the public arena, realizing (creating) dramaturgical novelty and emotional richness in discussing the problem, taking into account the organizational specifics of the public arena, directing interest in the problem towards other (related and equally important) public arenas (media, legislative and executive power, political parties).
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Gibbons, Michael, Alaina Puleo, Jill Herschleb, and Sarah Taylor. "Abstract 3392: Rapid, scalable isolation of human tumor nuclei for single cell genomics." Cancer Research 82, no. 12_Supplement (June 15, 2022): 3392. http://dx.doi.org/10.1158/1538-7445.am2022-3392.
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Abstract Sample preparation remains a significant roadblock to the implementation and scaling of single cell technologies in clinical and translational research. Time consuming, custom dissociation protocols performed on fresh tissue by experts are often utilized to obtain high quality single cell data. However, these complex protocols result in low sample throughput and high variability driven by logistical challenges and user variability. Additionally, many samples that are of interest for clinical and translational studies arrive in research labs snap frozen, leaving nuclei extraction as the only viable path to generate single cell/nuclei data. To overcome these challenges, we present a scalable, easy to use, spin column-based nuclei isolation platform that generates single nuclei that are ready for input into downstream 10x Genomics single cell assays in under an hour. We processed eight unique human tumors in parallel (skin melanoma, breast, ovary, prostate, lung, kidney, colorectal, and pancreas). Starting from snap frozen tissue, we were able to generate single nuclei suspensions for all samples, in parallel, within 90 minutes. The resulting single nuclei were run through the 10x Genomics Single Cell Gene Expression, Single Cell Immune Profiling, and Single Cell ATAC workflows. Single cell gene expression data retained high data complexity across all tumor types tested. Expected cell type clusters, including tissue-specific tumor cells, surrounding/infiltrating immune cells and stromal cells were easily identified and profiled via gene expression analysis. Even fragile cell types often missing in single-cell data (e.g. mature adipocytes) were preserved and profiled in this experiment. Additionally, epigenetic changes as measured by Single Cell ATAC profiling were consistent with both general and tissue-specific epigenetics of each cancer type analyzed. In summary, this platform streamlines sample preparation for single cell studies from snap frozen tissue, enabling these samples to be used much more routinely and unlock biobanked sample cohorts. The findings presented here demonstrate that this fast and easy to use nuclei isolation system provides a consistent platform for generating high quality single nuclei data at scale for clinical, translational and basic research studies alike. Citation Format: Michael Gibbons, Alaina Puleo, Jill Herschleb, Sarah Taylor. Rapid, scalable isolation of human tumor nuclei for single cell genomics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3392.
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Terrón-Camero, Laura C., Fernando Gordillo-González, Eduardo Salas-Espejo, and Eduardo Andrés-León. "Comparison of Metagenomics and Metatranscriptomics Tools: A Guide to Making the Right Choice." Genes 13, no. 12 (December 3, 2022): 2280. http://dx.doi.org/10.3390/genes13122280.
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The study of microorganisms is a field of great interest due to their environmental (e.g., soil contamination) and biomedical (e.g., parasitic diseases, autism) importance. The advent of revolutionary next-generation sequencing techniques, and their application to the hypervariable regions of the 16S, 18S or 23S ribosomal subunits, have allowed the research of a large variety of organisms more in-depth, including bacteria, archaea, eukaryotes and fungi. Additionally, together with the development of analysis software, the creation of specific databases (e.g., SILVA or RDP) has boosted the enormous growth of these studies. As the cost of sequencing per sample has continuously decreased, new protocols have also emerged, such as shotgun sequencing, which allows the profiling of all taxonomic domains in a sample. The sequencing of hypervariable regions and shotgun sequencing are technologies that enable the taxonomic classification of microorganisms from the DNA present in microbial communities. However, they are not capable of measuring what is actively expressed. Conversely, we advocate that metatranscriptomics is a “new” technology that makes the identification of the mRNAs of a microbial community possible, quantifying gene expression levels and active biological pathways. Furthermore, it can be also used to characterise symbiotic interactions between the host and its microbiome. In this manuscript, we examine the three technologies above, and discuss the implementation of different software and databases, which greatly impact the obtaining of reliable results. Finally, we have developed two easy-to-use pipelines leveraging Nextflow technology. These aim to provide everything required for an average user to perform a metagenomic analysis of marker genes with QIMME2 and a metatranscriptomic study using Kraken2/Bracken.
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Belarbi, Naima, Nadia Chafiq, Mohammed Talbi, Abdelwahed Namir, and Elhabib Benlahmar. "User Profiling in a SPOC: A method based on User Video Clickstream Analysis." International Journal of Emerging Technologies in Learning (iJET) 14, no. 01 (January 17, 2019): 110. http://dx.doi.org/10.3991/ijet.v14i01.9091.
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In the present paper, we address to construct a structured user profile in a Small Private Online Course (SPOC) based on user’s video clickstream analysis. We adopt an implicit approach to infer user’s preferences and experience difficulty based on user’s video sequence viewing analysis at the click-level as Play, Pause, Move forward… the Bayesian method is used in order to infer implicitly user’s interests. Learners with similar clickstream behavior are then segmented into clusters by using the unsupervised K-Means clustering algorithm. Videos that could meet the individual learner interests and offer a best and personalized experienced learning can therefore be recommended for a learner while enrolling in a SPOC based on his videos interactions and exploiting similar learners’ profiles.
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Rahman, Md Mizanur, Susane Giti, and Debashish Saha. "Flow Cytomerty: Clinical Applications in Haemato-Oncology." Journal of Armed Forces Medical College, Bangladesh 11, no. 1 (December 15, 2016): 74–80. http://dx.doi.org/10.3329/jafmc.v11i1.30677.
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In the past decade, the use of flow cytometry in the clinical haematology laboratory has grown substantially due to the development of smaller, user-friendly, less-expensive instruments and a continuous increase in the number of clinical applications. Multiple characteristics of single cells can be analyzed rapidly by flow cytometry. Both qualitative and quantitative information are obtained by flow cytometry whereas previously only in research institutions and esteemed academic centres flow cytometers were found. With advances in technology now it is possible for secondary level hospitals to use this methodology. This paper reviews the selected applications of flow cytometry in the clinical haematology laboratory in Bangladesh. This review serves to awaken the interest of stakeholders involved in the diagnosis and management of haematological malignancies (HM) in the efficacy of flow cytometry in the immunophenotypic characterization of leukaemias and lymphomas. Relevant literature including those provided by different international consensus groups on the phenotypic characterization of HM was reviewed. Additionally, recent reports on the immunophenotypic analysis of HM published in haematology, oncology, pathology, immunology and cell biology journals were also analyzed. Flow cytometric immunophenotyping of HM is highly demanding. It is highly useful in profiling the leukaemias and lymphomas and allows proper ramification along the latest WHO classification guidelines, thereby paving the way for targeted therapy and clinical trial-driven management, significantly outweighs the cost, which can be fully recovered if properly managed. In a low-resource setting like Bangladesh, limited immunohistochemistry serves to bridge the gap in technological advancement.Journal of Armed Forces Medical College Bangladesh Vol.11(1) 2015: 74-80
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GODOY, DANIELA, and ANALIA AMANDI. "User profiling in personal information agents: a survey." Knowledge Engineering Review 20, no. 4 (December 2005): 329–61. http://dx.doi.org/10.1017/s0269888906000397.
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Personal information agents have emerged in the last decade to help users to cope with the increasing amount of information available on the Internet. These agents are intelligent assistants that perform several information-related tasks such as finding, filtering and monitoring relevant information on behalf of users or communities of users. In order to provide personalized assistance, personal agents rely on representations of user information interests and preferences contained in user profiles. In this paper, we present a summary of the state-of-the-art in user profiling in the context of intelligent information agents. Existing approaches and lines of research in the main dimensions of user profiling, such as acquisition, learning, adaptation and evaluation, are discussed.
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Hu, Shuyue, Yi Cai, Ho-fung Leung, Dongping Huang, and Yang Yang. "Integrating User Reviews and Ratings for Enhanced Personalized Searching." International Journal of Distance Education Technologies 15, no. 2 (April 2017): 86–101. http://dx.doi.org/10.4018/ijdet.2017040106.
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With the development of e-commerce, websites such as Amazon and eBay have become very popular. Users post reviews of products and rate the helpfulness of reviews on these websites. Reviews written by a user and reviews rated by a user reflect the user's interests and disinterest. Thus, they are very useful for user profiling. In this study, the authors explore users' reviews and ratings of reviews for personalized searching and propose a review-based user profiling method. To satisfy a user's basic information needs, expressed in the form of a query, they also propose a priority-based result ranking strategy. For evaluation, they conduct experiments on a real-life data set. The experimental results show that their method can significantly improve retrieval quality.
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Столярова, Валерия Фуатовна, Александра Витальевна Торопова, and Александр Львович Тулупьев. "A Model for Estimating the Posting Frequency in an Online Social Media with Incomplete Data Using Objective Determinants of Users’ Behaviour." Fuzzy Systems and Soft Computing, no. 2 (December 28, 2021): 77–95. http://dx.doi.org/10.26456/fssc81.
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Профилирование пользователя онлайн социальной сети включает задачу оценки частоты (интенсивности) различных действий, в частности, публикации постов. Однако в силу ресурсных ограничений, может быть доступна только неполная информация о времени публикации нескольких последних постов, полученная, например, в рамках интервью. Оценка интенсивности постинга на основании таких данных востребована при анализе индивидуального риска, связанного с использованием онлайн социальных сетей. В статье предложена расширенная байесовская сеть доверия, которая использует не только информацию о времени публикации последних постов, но и объективные данные из профиля пользователя: пол, возраст, число друзей. Для обучения и демонстрации работы модели были собраны данные о публикации постов случайных пользователей в онлайн социальной сети ВКонтакте. Расширенная структура имеет более высокое значение информационного критерия Акаике по сравнению с упрощенной. User profiling is related to the problem of estimation of frequency of certain user’s actions in an online social media, like posting. But due to limited resources the only information available may be imprecise information on several last episodes of posting, that can be gathered via an interview. The frequency of posting estimates with such limited data may be used in the individual risk assessment that is connected with the use of online social media, for example, in medicine or cybersecurity. In the paper the Bayes belief network (BBN) for this problem is constructed, that incorporates not only the limited data on times of several last posts in an online social media, but the objective data about the user’s profile: age, sex, and friends count. With the training dataset gathered via API VKontakte we estimated conditional probability tables for two expert BBN structures (existing reduced structure based only on dates of several last posts and novel extended structure with objective behavior determinants incorporated) and automatically learned the optimal structure for the training data. Both extended models (expert and learned) showed lower values of the information criteria (Akaike information criteria and bayesian information criteria). Then with the test dataset the classification problem of the true frequency value was assessed. All three models showed similar results based on accuracy, kappa and average accuracy characteristics. This result is related to the weak strength of arcs between frequency variable and objective behavior determinants. But nevertheless the use of such variables is important in the application in order to construct the comprehensive structure of the knowledge in the area of interest. The practical significance of the work lies in the possibility of applying the proposed model to assess the posting frequency in the online social network, in particular in the tasks of modeling risk in the field of public health and socio-cybersecurity.
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Cimadomo, Danilo, Laura Rienzi, Adriano Giancani, Erminia Alviggi, Ludovica Dusi, Rita Canipari, Laila Noli, et al. "Definition and validation of a custom protocol to detect miRNAs in the spent media after blastocyst culture: searching for biomarkers of implantation." Human Reproduction 34, no. 9 (August 16, 2019): 1746–61. http://dx.doi.org/10.1093/humrep/dez119.
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Abstract STUDY QUESTION Can miRNAs be reliably detected in the spent blastocyst media (SBM) after IVF as putative biomarkers of the implantation potential of euploid embryos? SUMMARY ANSWER Adjustment of the data for blastocyst quality and the day of full-expansion hinders the predictive power of a fast, inexpensive, reproducible and user-friendly protocol based on the detection of 10 selected miRNAs from SBM. WHAT IS KNOWN ALREADY Euploidy represents so far the strongest predictor of blastocyst competence. Nevertheless, ~50% of the euploid blastocysts fail to implant. Several studies across the years have suggested that a dialogue exists between the embryo and the endometrium aimed at the establishment of a pregnancy. MicroRNAs have been proposed as mediators of such a dialogue and investigated in this respect. Several expensive, time-consuming and complex protocols have been adopted and promising results have been produced, but conclusive evidence from large clinical studies is missing. STUDY DESIGN, SIZE, DURATION This study was conducted in two phases from September 2015 to December 2017. In Phase 1, the human blastocyst miRNome profile was defined from the inner cell mass (ICM) and the corresponding whole-trophectoderm (TE) of six donated blastocysts. Two different protocols were adopted to this end. In parallel, 6 pools of 10 SBM each were run (3 from only implanted euploid blastocysts, IEBs; and 3 from only not-implanted euploid blastocysts, not-IEBs). A fast, inexpensive and user-friendly custom protocol for miRNA SBM profiling was designed. In Phase 2, 239 SBM from IEB and not-IEB were collected at three IVF centres. After 18 SBM from poor-quality blastocysts were excluded from the analysis, data from 107 SBM from not-IEB and 114 from IEB were produced through the previously developed custom protocol and compared. The data were corrected through logistic regressions. PARTICIPANT/MATERIALS, SETTINGS, METHODS Donated blastocysts underwent ICM and whole-TE isolation. SBM were collected during IVF cycles characterized by ICSI, blastocyst culture in a continuous media, TE biopsy without zona pellucida opening in Day 3, quantitative PCR (qPCR)-based aneuploidy testing and vitrified-warmed single euploid embryo transfer. Not-IEB and IEB were clustered following a negative pregnancy test and a live birth, respectively. The Taqman Low Density Array (TLDA) cards and the Exiqon microRNA human panel I+II qPCR analysis protocols were adopted to analyse the ICM and whole-TE. The latter was used also for SBM pools. A custom protocol and plate was then designed based on the Exiqon workflow, validated and finally adopted for SBM analysis in study Phase 2. This custom protocol allows the analysis of 10 miRNAs from 10 SBM in 3 hours from sample collection to data inspection. MAIN RESULTS AND ROLE OF THE CHANCE The TLDA cards protocol involved a higher rate of false positive results (5.6% versus 2.8% with Exiqon). There were 44 miRNAs detected in the ICM and TE from both the protocols. One and 24 miRNAs were instead detected solely in the ICM and the TE, respectively. Overall, 29 miRNAs were detected in the pooled SBM: 8 only from not-IEB, 8 only from IEB and 13 from both. Most of them (N = 24/29, 82.7%) were also detected previously in both the ICM and TE with the Exiqon protocol; two miRNAs (N = 2/29, 6.9%) were previously detected only in the TE, and three (N = 3/29, 10.3%) were never detected previously. In study Phase 2, significant differences were shown between not-IEB and IEB in terms of both miRNA detection and relative quantitation. However, when the data were corrected for embryo morphology and day of full development (i.e. SBM collection), no significant association was confirmed. LIMITATIONS, REASONS FOR CAUTION This study did not evaluate specifically exosomal miRNAs, thereby reducing the chance of identifying the functional miRNAs. Ex-vivo experiments are required to confirm the role of miRNAs in mediating the dialogue with endometrial cells, and higher throughput technologies need to be further evaluated for miRNA profiling from clinical SBM samples. WIDER IMPLICATIONS OF THE FINDINGS Although no clinical predictive power was reported in this study, the absence of invasiveness related with SBM analysis and the evidence that embryonic genetic material can be reliably detected and analysed from SBM make this waste product of IVF an important source for further investigations aimed at improving embryo selection. STUDY FUNDING/COMPETING INTEREST(S) This project has been financially supported by Merck KgaA (Darmstadt, Germany) with a Grant for Fertility Innovation (GFI) 2015. The authors have no conflict of interest to declare related with this study. TRIAL REGISTRATION NUMBER None.
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Sahoo, Sipra, and Bikram Kesari Ratha. "User profiling for web personalization using multi agent and DBSCAN based approach." International Journal of Engineering & Technology 7, no. 2 (June 1, 2018): 849. http://dx.doi.org/10.14419/ijet.v7i2.10224.
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The user experience is enhanced by the Web Personalization System (WPS), which depends on the User's Interests (UI) and references are stored in the User Profile (UP). The profiles should be able to adapt and reproduce the change of user’s behavior for such system. Existing web page Recommendation Systems (RS) are still limited by several problems, some of which are the problem of recommending web pages to a new user whose browsing history is not available (Cold Start), sparse data structures (Sparsity), and the problem of over-specialization. In this paper, the UI has been tracked and Dynamic User Profiles have been maintained by introducing a method called Density-Based Spa-tial Clustering of Applications with Noise-User Profiling (DBSCAN-UP). The mapping web pages, construct the ontological concepts, which represent the UI, and the interests of users are learned by the reference ontology, which are used to map the visited web pages. The process of storage, management and adaptation of UI is facilitated by multi-agent system. The different user browsing behaviors learning and adapting capability is built in the proposed system and the efficiency of the DBSCAN-UP model is evaluated by the series of experi-ments. The accuracy of the DBSCAN-UP was achieved up to 5% compared to the existing methods.
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Orlandi, Fabrizio. ""Profiling user interests on the social semantic web" by Fabrizio Orlandi with Prateek Jain as coordinator." ACM SIGWEB Newsletter, Spring (April 2014): 1. http://dx.doi.org/10.1145/2591453.2591456.
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Huang, Ko-Hsun, Yi-Shin Deng, and Ming-Chuen Chuang. "Static and Dynamic User Portraits." Advances in Human-Computer Interaction 2012 (2012): 1–16. http://dx.doi.org/10.1155/2012/123725.
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User modeling and profiling has been used to evaluate systems and predict user behaviors for a considerable time. Models and profiles are generally constructed based on studies of users’ behavior patterns, cognitive characteristics, or demographic data and provide an efficient way to present users’ preferences and interests. However, such modeling focuses on users’ interactions with a system and cannot support complicated social interaction, which is the emerging focus of serious games, educational hypermedia systems, experience, and service design. On the other hand, personas are used to portray and represent different groups and types of users and help designers propose suitable solutions in iterative design processes. However, clear guidelines and research approaches for developing useful personas for large-scale and complex social networks have not been well established. In this research, we reflect on three different design studies related to social interaction, experience, and cross-platform service design to discuss multiple ways of identifying both direct users and invisible users in design research. In addition, research methods and attributes to portray users are discussed.
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Gu, Jie, Feng Wang, Qinghui Sun, Zhiquan Ye, Xiaoxiao Xu, Jingmin Chen, and Jun Zhang. "Exploiting Behavioral Consistence for Universal User Representation." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 5 (May 18, 2021): 4063–71. http://dx.doi.org/10.1609/aaai.v35i5.16527.
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User modeling is critical for developing personalized services in industry. A common way for user modeling is to learn user representations that can be distinguished by their interests or preferences. In this work, we focus on developing universal user representation model. The obtained universal representations are expected to contain rich information, and be applicable to various downstream applications without further modifications (e.g., user preference prediction and user profiling). Accordingly, we can be free from the heavy work of training task-specific models for every downstream task as in previous works. In specific, we propose Self-supervised User Modeling Network (SUMN) to encode behavior data into the universal representation. It includes two key components. The first one is a new learning objective, which guides the model to fully identify and preserve valuable user information under a self-supervised learning framework. The other one is a multi-hop aggregation layer, which benefits the model capacity in aggregating diverse behaviors. Extensive experiments on benchmark datasets show that our approach can outperform state-of-the-art unsupervised representation methods, and even compete with supervised ones.